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» Optimizing Learning in Image Retrieval
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SIGIR
2008
ACM
13 years 5 months ago
Learning to reduce the semantic gap in web image retrieval and annotation
We study in this paper the problem of bridging the semantic gap between low-level image features and high-level semantic concepts, which is the key hindrance in content-based imag...
Changhu Wang, Lei Zhang 0001, Hong-Jiang Zhang
TIP
2010
155views more  TIP 2010»
13 years 3 months ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He
ICIP
2006
IEEE
14 years 6 months ago
Precision-Oriented Active Selection for Interactive Image Retrieval
Active learning methods have been considered with an increased interest in the content-based image retrieval (CBIR) community. Those methods used to be based on classical classifi...
Philippe Henri Gosselin, Matthieu Cord
CVPR
2009
IEEE
15 years 5 days ago
Learning query-dependent prefilters for scalable image retrieval
We describe an algorithm for similar-image search which is designed to be efficient for extremely large collections of images. For each query, a small response set is selected by...
Lorenzo Torresani (Dartmouth College), Martin Szum...
MIR
2004
ACM
171views Multimedia» more  MIR 2004»
13 years 10 months ago
Mean version space: a new active learning method for content-based image retrieval
In content-based image retrieval, relevance feedback has been introduced to narrow the gap between low-level image feature and high-level semantic concept. Furthermore, to speed u...
Jingrui He, Hanghang Tong, Mingjing Li, HongJiang ...